Hybrid flow-shop scheduling problem (HFSP) is widely used in the field of intelligent manufacturing, which brings a variety of environmental benefits to the energy consumption problem of workshop through resource integration. In order to explore the energy-efficient potential of manufacturing workshop, an improved multi-objective multi-verse optimizer (IMOMVO) algorithm is proposed to solve the proposed multi-objective optimization HFSP model for energy-efficient based on mixed integer linear programming (MILP) model. In addition, a "shutdown-restart" energy-efficient strategy is proposed, which can reduce the energy consumption of the manufacturing workshop and prolong the life of the machine. Finally, through a test example, three manufacturing scenarios are designed. The improved genetic-simulated annealing algorithm (IGSA) in literature is compared with IMOMVO and the results validate the effectiveness and superiority of the proposed model and algorithm.